Elastic resource management in cloud computing platforms

Large scale enterprise applications are known to observe dynamic workload; provisioning correct capacity for these applications remains an important and challenging problem. Predicting high variability fluctuations in workload or the peak workload is difficult; erroneous predictions often lead to under-utilized systems or in some situations cause temporarily outage of an otherwise well provisioned web-site. Consequently, rather than provisioning server capacity to handle infrequent peak workloads, an alternate approach of dynamically provisioning capacity on-the-fly in response to workload fluctuations has become popular.;Cloud platforms are particularly suited for such applications due to their ability to provision capacity when needed and charge for usage on pay-per-use basis. Cloud environments enable elastic provisioning by providing a variety of hardware configurations as well as mechanisms to add or remove server capacity.;The first part of this thesis presents Kingfisher, a cost-aware system that provides a generalized provisioning framework for supporting elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimize cost.;Majority of these enterprise applications, deployed as web applications, are distributed or replicated with a multi-tier architecture. SLAs for such applications are often expressed as a high percentile of a performance metric, for e.g. 99 percentile of end to end response time is less than 1 sec. In the second part of this thesis I present a model driven technique which provisions a multi-tier application for such an SLA and is targeted for cloud platforms.;Enterprises critically depend on these applications and often own large IT infrastructure to support the regular operation of these applications. However, provisioning for a peak load or for high percentile of response time could be prohibitively expensive. Thus there is a need of hybrid cloud model, where the enterprise uses its own private resources for the majority of its computing, but then "bursts" into the cloud when local resources are insufficient. I discuss a new system, namely Seagull, which performs dynamic provisioning over a hybrid cloud model by enabling cloud bursting.;Finally, I describe a methodology to model the configuration patterns (i.e deployment topologies) of different control plane services of a cloud management system itself. I present a generic methodology, based on empirical profiling, which provides initial deployment configuration of a control plane service and also a mechanism which iteratively adjusts the configuration to avoid violation of control plane's Service Level Objective (SLO).